Research News

Computer system can spot real or faked expressions of pain better than people

While the ability to distinguish between real and faked expressions of pain has obvious uses for uncovering pain malingering — fabricating or exaggerating the symptoms of pain for a variety of motives — the system also could be used to detect deceptive actions in the realms of security, psychopathology, job screening, medicine and law.

By PATRICIA DONOVAN

“Human subjects could not discriminate real from faked expressions of pain more frequently than would be expected by chance. Even after training, they were accurate only 55 percent of the time. The computer system, however, was accurate 85 percent of the time.”

Mark Frank, professor

Department of Communication

A joint study by researchers at the University of California,
San Diego; UB; and the University of Toronto has found that a
computer–vision system can distinguish between real or faked
expressions of pain more accurately than can humans.

This ability has obvious uses for uncovering pain malingering
— fabricating or exaggerating the symptoms of pain for a
variety of motives — but the system also could be used to
detect deceptive actions in the realms of security,
psychopathology, job screening, medicine and law.

The study, “Automatic Decoding of Deceptive Pain
Expressions,” appears in the latest issue of Current
Biology.

The authors are Marian Bartlett, research professor, Institute
for Neural Computation, University of California, San Diego; Gwen
C. Littlewort, co-director of the institute’s Machine
Perception Laboratory; Mark G. Frank, UB professor of
communication; and Kang Lee, Dr. Erick Jackman Institute of Child
Study, University of Toronto.

The study employed two experiments with a total of 205 human
observers who were asked to assess the veracity of expressions of
pain in video clips of individuals, some of whom were being
subjected to the cold presser test in which a hand is immersed in
ice water to measure pain tolerance, and others of whom were faking
their painful expressions.

“Human subjects could not discriminate real from faked
expressions of pain more frequently than would be expected by
chance,” Frank says. “Even after training, they were
accurate only 55 percent of the time. The computer system, however,
was accurate 85 percent of the time.”

Bartlett noted that the computer system “managed to detect
distinctive, dynamic features of facial expressions that people
missed. Human observers just aren’t very good at telling real
from faked expressions of pain.”

The researchers employed the computer expression recognition
toolbox (CERT), an end-to-end system for fully automated
facial-expression recognition that operates in real-time. It was
developed by Bartlett, Littlewort, Frank and others to assess the
accuracy of machine versus human vision.

They found that machine vision was able to automatically
distinguish deceptive facial signals from genuine facial signals by
extracting information from spatio-temporal facial-expression
signals that humans either cannot or do not extract.

“In highly social species such as humans,” says Lee,
“faces have evolved to convey rich information, including
expressions of emotion and pain. And, because of the way our brains
are built, people can simulate emotions they’re not actually
experiencing so successfully that they fool other people. The
computer is much better at spotting the subtle differences between
involuntary and voluntary facial movements.”

Bartlet says this approach illuminates basic questions
pertaining to many social situations in which the behavioral
fingerprint of neural control systems may be relevant.

“As with causes of pain, these scenarios also
generate strong emotions, along with attempts to minimize, mask and
fake such emotions, which may involve ‘dual control’ of
the face,” Bartlett says.

“Dual control of the face means that the signal for our
spontaneous felt emotion expressions originate in different areas
in the brain than our deliberately posed emotion
expressions,” Frank explains, “and they proceed through
different motor systems that accounts for subtle appearance, and in
the case of this study, dynamic movement factors.”

The computer-vision system, Bartlett says, “can be applied
to detect states in which the human face may provide important
clues as to health, physiology, emotion or thought, such as
drivers’ expressions of sleepiness, students’
expressions of attention and comprehension of lectures, or
responses to treatment of affective disorders.”

The single most predictive feature of falsified expressions, the
study showed, is how and when the mouth opens and closes.
Fakers’ mouths open with less variation and too regularly.
The researchers say further investigations will explore whether
such over-regularity is a general feature of fake expressions.

Please leave blank

Comments

The UB Reporter welcomes comments from its readers. Please
submit your comments in the box below.